class_docstr: Broyden-Fletcher-Goldfarb-Shanno (BFGS) Hessian update strategy.
  
  Parameters
  ----------
  exception_strategy : {'skip_update', 'damp_update'}, optional
      Define how to proceed when the curvature condition is violated.
      Set it to 'skip_update' to just skip the update. Or, alternatively,
      set it to 'damp_update' to interpolate between the actual BFGS
      result and the unmodified matrix. Both exceptions strategies
      are explained  in [1]_, p.536-537.
  min_curvature : float
      This number, scaled by a normalization factor, defines the
      minimum curvature ``dot(delta_grad, delta_x)`` allowed to go
      unaffected by the exception strategy. By default is equal to
      1e-8 when ``exception_strategy = 'skip_update'`` and equal
      to 0.2 when ``exception_strategy = 'damp_update'``.
  init_scale : {float, 'auto'}
      Matrix scale at first iteration. At the first
      iteration the Hessian matrix or its inverse will be initialized
      with ``init_scale*np.eye(n)``, where ``n`` is the problem dimension.
      Set it to 'auto' in order to use an automatic heuristic for choosing
      the initial scale. The heuristic is described in [1]_, p.143.
      By default uses 'auto'.
  
  Notes
  -----
  The update is based on the description in [1]_, p.140.
  
  References
  ----------
  .. [1] Nocedal, Jorge, and Stephen J. Wright. "Numerical optimization"
         Second Edition (2006).
docstr: Update the Hessian matrix.
  
  BFGS update using the formula:
  
      ``B <- B - (B*s)*(B*s).T/s.T*(B*s) + y*y^T/s.T*y``
  
  where ``s`` is short for ``delta_x`` and ``y`` is short
  for ``delta_grad``. Formula (6.19) in [1]_.
  
  References
  ----------
  .. [1] Nocedal, Jorge, and Stephen J. Wright. "Numerical optimization"
         Second Edition (2006).
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code: def _update_hessian(self, ys, Bs, sBs, y):
      """Update the Hessian matrix.
  
      BFGS update using the formula:
  
          ``B <- B - (B*s)*(B*s).T/s.T*(B*s) + y*y^T/s.T*y``
  
      where ``s`` is short for ``delta_x`` and ``y`` is short
      for ``delta_grad``. Formula (6.19) in [1]_.
  
      References
      ----------
      .. [1] Nocedal, Jorge, and Stephen J. Wright. "Numerical optimization"
             Second Edition (2006).
      """
      self.B = self._syr(1.0 / ys, y, a=self.B)
      self.B = self._syr(-1.0 / sBs, Bs, a=self.B)
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy.linalg import norm
  from scipy.linalg import get_blas_funcs
  from warnings import warn
label_desc: Use image node_id 3 for calling the BFGS obj's underlying member method code with example usage: obj._update_hessian(ys, Bs, sBs, y) without return types